Communications of the ACM
Latest Research
From Communications of the ACM
Almost-Linear-Time Algorithms for Maximum Flow and Minimum-Cost Flow
We present an algorithm that computes exact maximum flows and minimum-cost flows on directed graphs with m edges and polynomially bounded integral demands, costs...
From Communications of the ACM
Technical Perspective: A Rare Glimpse of Tracking Fake Reviews
"Leveraging Social Media to Buy Fake Reviews," by Sherry He et al., represents a breakthrough in our empirical understanding of fake reviews on Amazon.
From Communications of the ACM
Leveraging Social Media to Buy Fake Reviews
We study the market for fake product reviews on Amazon.com.
From Communications of the ACM
Technical Perspective: Tapping the Link between Algorithmic Model Counting and Streaming
"Model Counting Meets Distinct Elements," by A. Pavan et al., gives a surprising connection between model counting and streaming, providing a generic transformation...
From Communications of the ACM
Technical Perspective: Opening the Door to SSD Algorithmics
The authors of "Offline and Online Algorithms for SSD Management" propose a more accurate theoretical model of flash-based SSDs that views each page as containing...
From Communications of the ACM
Offline and Online Algorithms for SSD Management
We explore the problem of reducing high internal overhead of flash media which is referred to as write amplification from an algorithmic perspective, considering...
From Communications of the ACM
Technical Perspective: FoundationDB Performs Balancing Act
FoundationDB, as explored in "FoundationDB: A Distributed Key-Value Store," by Jingyu Zhou et al., pioneered the development of a scalable distributed key-value...
From Communications of the ACM
FoundationDB: A Distributed Key-Value Store
FoundationDB, an open-source transactional key-value store, is one of the first systems to combine the flexibility and scalability of NoSQL architectures with the...
From Communications of the ACM
Technical Perspective: Beautiful Symbolic Abstractions for Safe and Secure Machine Learning
"Proving Data-Poisoning Robustness in Decision Trees," by Samuel Drews et al., addresses the challenge of processing an intractably large set of trained models...
From Communications of the ACM
Technical Perspective: The Power of Low-Power GPS Receivers for Nanosats
The work explored in "Hummingbird," by Sujay Narayana et al., focuses on the energy consumption of a typical GPS receiver and its operational challenges in a nanosat...
From Communications of the ACM
Hummingbird: An Energy-Efficient GPS Receiver for Small Satellites
In this work, we elucidate the design of a low-cost, low-power GPS receiver for small satellites.
From Communications of the ACM
Sampling Near Neighbors in Search for Fairness
We propose several efficient data structures for the exact and approximate variants of the fair near neighbor problem.
From Communications of the ACM
Technical Perspective: Can Data Structures Treat Us Fairly?
In "Sampling Near Neighbors in Search for Fairness," Aumüller et al. investigate a basic problem in similarity search called near neighbor in the context of fair...
From Communications of the ACM
Technical Perspective: Visualization Search: From Sketching to Natural Language
"Expressive Querying for Accelerating Visual Analytics," by Tarique Siddiqui et al., provides a general abstraction, along with advanced interfaces, focusing on...
From Communications of the ACM
Expressive Querying for Accelerating Visual Analytics
In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching.
From Communications of the ACM
Technical Perspective: Evaluating Sampled Metrics Is Challenging
"On Sampled Metrics for Item Recommendation," by Walid Krichene and Steffen Rendle, exposes a crucial aspect for the evaluation of algorithms and tools: the impact...
From Communications of the ACM
On Sampled Metrics for Item Recommendation
This paper investigates sampled metrics and shows that it is possible to improve the quality of sampled metrics by applying a correction, obtained by minimizing...
From Communications of the ACM
Technical Perspective: Leveraging Social Context for Fake News Detection
In "FANG," the authors focus on a strategy of automatically detecting disinformation campaigns on online media with a new graph-based, contextual technique for...
From Communications of the ACM
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection.
From Communications of the ACM